Function reference • homelocator

All functions
`arrange_double_nested()`	Arrange rows by variables within a double-nested dataframe
`arrange_nested()`	Arrange rows by variables within a nested dataframe
`df_neighbors`	Neighbours for locations
`enrich_timestamp()`	Create new variables dervied from timestamp
`extract_location()`	Extract identified home locations for users
`filter_nested()`	Return rows with matching condition within nested dataframe
`filter_verbose()`	Return rows with matching conditions
`identify_location()`	Identify home locations for users with built-in recipes
`mutate_double_nested()`	Add new variable within a double-nested dataframe
`mutate_nested()`	Add new variables within a nested dataframe
`mutate_verbose()`	Add new variable
`nest_nested()`	Nest within a nested dataframe
`nest_verbose()`	Nest dataframe
`prop_factor_nested()`	Calculate the proportion of categories for a variable within a nested dataframe
`recipe_APDM()`	recipe: Anchor Point Determining Model - APDM
`recipe_FREQ()`	recipe: frequency - FREQ
`recipe_HMLC()`	recipe: homelocator - HMLC
`recipe_OSNA()`	recipe: Online Social Networks Activity - OSNA
`remove_top_users()`	Remove top N percent of active users based on the frequency of data points per user
`score_nested()`	Give a weighted value for one or more variables in a nested dataframe
`score_summary()`	Summarises all scored columns and return one single summary score per row
`spread_nested()`	Spread a key-value pair across multiple columns
`summarise_double_nested()`	Aggregate multiple values to a single value in a double-nested tibble
`summarise_nested()`	Aggregate multiple values to a single value within a nested tibble
`test_sample`	Tweets sent by 100 random users
`top_n_nested()`	Select top n rows by certain value
`unnest_double_nested()`	Unnest within a double-nested dataframe
`unnest_nested()`	Unnest within a nested dataframe
`unnest_verbose()`	Unnest dataframe
`validate_dataset()`	Validate input dataset

Reference

All functions